THE inverse tangent function is an elementary mathematical

Size: px
Start display at page:

Download "THE inverse tangent function is an elementary mathematical"

Transcription

1 A Sharp Double Inequality for the Inverse Tangent Function Gholamreza Alirezaei arxiv: v [cs.it] 8 Jul 03 Abstract The inverse tangent function can be bounded by different inequalities, for eample by Shafer s inequality. In this publication, we propose a new sharp double inequality, consisting of a lower an upper bound, for the inverse tangent function. In particular, we sharpen Shafer s inequality calculate the best corresponding constants. The maimum relative errors of the obtained bounds are approimately smaller than 0.7% 0.3% for the lower upper bound, respectively. Furthermore, we determine an upper bound on the relative errors of the proposed bounds in order to describe their tightness analytically. Moreover, some important properties of the obtained bounds are discussed in order to describe their behavior achieved accuracy. Inde Terms trigonometric bounds; Shafer s inequality; inverse tangent approimation; I. INTRODUCTION THE inverse tangent function is an elementary mathematical function that appears in many applications, especially in different fields of engineering. In electrical engineering, especially in the communication theory signal processing, it is mostly used to describe the phase of a comple-valued signal. But there are many other applications in which the inverse tangent function plays an important role. On the one h, it is often used as an approimation for more comple functions because of its elementary behavior. For instance, the Heaviside step function is the most famous function that can be very accurately approimated by the inverse tangent function. On the other h, it is sometimes approimated by simpler functions in order to enable further calculations. For instance, the inverse tangent function can be accurately approimated by its argument if the absolute value of the argument is sufficiently small. Quite naturally the problem arises how to replace the inverse tangent function with a surrogate function, in order to approimate the inverse tangent function as well as other contemplable functions accurately. If a surrogate function with a mathematically simple form could be found, then the subsequent application of such a surrogate function would be considerable. A few application cases are in the field of information estimation theory where an unknown phase shift or the direction of arrival is estimated, for eample by using the CORDIC-algorithm [], the MUSIC-algorithm [], or MAP ML estimators [3]. Some other cases are in the field of system design control theory where a non-linear network unit is modeled by a non-linear function, for instance G. Alirezaei is with the Institute for Theoretical Information Technology, RWTH Aachen University, 5056 Aachen, Germany ( alirezaei@ti.rwthaachen.de). The present work is categorized in terms of Mathematics Subject Classification (MSC00): 6D05, 6D07, 6D5, 33B0, 39B6. the saturation behavior of an amplifier [] or the sigmoidal non-linearity in neuronal networks [5]. Some more application cases are related to the theory of signals systems, where a signal should be mapped into a set of coefficients of basis functions, however the transformation is not feasible because of the phase description by the inverse tangent function, for eample in some Fourier-related transforms [6]. Many other applications are likewise conceivable. But we have to mention that finding a simple replacement for the inverse tangent function is in fact difficult. In the present work, we thus focus only on a special idea which has some nice properties is described in the following. In [7], R. E. Shafer proposed the elementary problem: Show that for all > 0 the inequality 3 < arctan() () holds, where arctan() denotes the inverse tangent function that is defined for all real numbers. From Shafar s problem several inequalities have been emerged to date. In particular, the authors in [8] investigated double inequalities of the form a a < arctan() < b b, > 0, () they determined the coefficients a, a, b b such that the above double inequality is sharp. In the present work, we follow a similar idea investigate a generalized version of the double inequality in (). We consider functions of the type c c c 3 (3) with positive real coefficients c, c c 3, because such kind of functions has advantageous properties in order to replace the inverse tangent function as we will discuss in the net section. Then we determine the triple (c, c, c 3 ) such that a lower an upper bound for the inverse tangent function is achieved. In order to describe the tightness of the obtained bounds, we determine an upper bound on the relative errors of the proposed bounds. Furthermore, we discuss some corresponding properties of the proposed bounds visualize the achieved results. G. A. July 8, 03 Mathematical Notations: Throughout this paper we denote the set of real numbers by R. The mathematical operation denotes the absolute value of any real number. Furthermore, O ( ω() ) denotes the order of any function ω().

2 II. MAIN THEOREMS In the current section, we present the new bounds for the inverse tangent function describe some of their important properties. Theorem II. For all R, let, h() be defined by := ( ), () := arctan() (5) h() := ( 6 6 ). (6) Then, for all R, the double inequality holds. h() (7) Proof: See Appendi A. Remark II. The functions, h() are point symmetric such that the identities f( ) =, g( ) = h( ) = h() hold. Hence, it is sufficient to consider only the case of 0. ( Remark II.3 The triples ) ( ), ( ), 6, ( 6 ), are the best possible ones such that the above double inequality holds. In other words, no component of the first triple can be replaced by a smaller value no component of the second triple can be replaced by a larger value with respect to 0 while keeping the other components fied. In this sense, the double inequality in Theorem II. is sharp. We get a first impression of the nature of the bounds from Figure. As we can see the double inequality in Theorem II. is very tight. The curves seem to be continuous, strictly increasing conve. Hence, we elaborately discuss the mathematical properties of the obtained bounds in the following. On the one h, the first three elements in the Taylor series epansions of, h() as approaches zero are obtained as ( ) 3 5 O ( 7), (8) O ( 7) (9) h() O ( 7). (0) Remark II. Only the both first elements in the Taylor series epansions of are identical to each other, while in the Taylor series epansions of h() the both h() Fig.. The inverse tangent function its bounds from Theorem II. are visualized for the range of 0 0. The curves are closely adjacent to one another such that without magnification the differences are not really visible. The curves are equal at zero approach the same upper limit as approaches infinity. first two elements are pairwise identical to each other. Thus, h() achieves a better approimation of than for sufficiently small. On the other h, the first three elements in the asymptotic power series epansions of, h() as approaches infinity are obtained as O ( 3), () 3 3 O( 5) () h() O ( 3) (3) by using the general definition of the asymptotic power series epansion [9, p., Definition.3.3] simple calculations. Remark II.5 The both first two elements in the asymptotic power series epansions of are pairwise identical to each other while in the asymptotic power series epansions of h() only the both first elements are identical to each other. Thus, achieves a better approimation of than h() for sufficiently large. Corollary II.6 From equations (8) (3) we conclude that lim = lim = lim h() = 0 () ±0 ±0 ±0 lim = ± lim = ± lim h() = ± ±. (5) Lemma II.7 For all R, both bounds h() are continuous.

3 3 Proof: Both numerators denominators of h() are continuous functions in the denominators are always non-zero which imply the absence of discontinuities. Lemma II.8 For all R, both bounds h() are strictly increasing. Proof: By differentiation we obtain the following first derivative d d c c c 3 = c c c c 3 c c 3 [ c c c 3 ]. (6) This derivative is positive for all R because c, c c 3 are positive constants in both bounds. Hence, the bounds are strictly increasing. Corollary II.9 Both bounds h() are differentiable on R, because the first derivative of the bounds eists due to the derivative in equation (6). Corollary II.0 Both bounds h() are limited, due to equation (5) because of the monotonicity in Lemma II.8. Corollary II. Both bounds h() do not have any critical points, because they are strictly increasing differentiable on R. Lemma II. For all 0, both bounds h() are concave while for all 0, both bounds are conve. Proof: By differentiation we obtain the following second derivative d d c c c 3 = c 3 3c c c c 3 3c c c 3 [ (c c 3 ) 3 / c c c 3 ] 3. (7) The sign of this derivative is only dependent on because c, c c 3 are positive constants in both bounds. Hence, this derivative is non-positive for all 0 non-negative for all 0 which completes the proof. Corollary II.3 Both bounds h() have the same unique inflection point at the origin, due to opposing conveities for 0 0, see Lemma II.. In the following enumeration, we now summarize the properties of the bounds that have been shown, thus far. ) The bounds are equal only at zero they approach the same limit as approaches infinity. ) Both bounds are point symmetric, continuous, strictly increasing, differentiable, limited. 3) They are conve for all 0 concave otherwise. ) There are no critical points. 5) Both bounds have the same unique inflection point h() f () f () r f () r h () h() f () h()f () Fig.. The relative errors of the bounds for the inverse tangent function are visualized for the range of 0 0. All curves are equal at zero they approach zero as approaches infinity. r h () is smaller, hence, better than r f () for sufficiently small values of, while for sufficiently large values of the opposite holds. The relative error r f () is approimately smaller than 0.7% while r h () is approimately smaller than 0.3%. The ratio [h() ]/[h()] lies between r f () r h (), hence, is an upper bound for min{r f (), r h ()} while []/ is an upper bound for ma{r f (), r h ()}. The above properties enable us to use the proposed bounds suitable in future works. It remains to show the tightness of the bounds with respect to the inverse tangent function. For this purpose we deduce an upper-bound on the actual relative errors of the bounds, in the following. Definition II. For all R, the relative errors of the bounds given in Theorem II. are defined by r f () := r h () := h() (8). (9) Note that = 0 is a removable singularity for both last ratios because of approimations (8), (9) (0). Thus, r f () r h () are continuously etendable over = 0. All following fractions are also continuously etendable over = 0 in a similar manner so that no difficulties related to singularities occur, hereinafter. Theorem II.5 For all R, the inequalities ma { r f (), r h () } = 0 9 ( ) 6 (0) 9

4 min { r f (), r h () } = 0 9 ( ) 9 ( ) hold. ma { r f (), r h () } () Proof: See Appendi B. Note, that the inequalities in Theorem II.5 do not contain the inverse tangent function, at all. In Figure, the relative errors of the obtained bounds are shown. The maimum relative errors of the bounds are approimately smaller than 0.7% 0.3% for h(), respectively. It is worthwhile mentioning that both bounds are valid for the whole domain of real numbers. III. CONCLUSION In the present work, we have investigated the approimation of the inverse tangent function deduced two new bounds. We have derived a lower an upper bound with simple closed-form formulae which are sharp very accurate. Furthermore, we have presented some useful important properties of the obtained bounds. These properties can be necessary in future works. Moreover, we have investigated the relative errors of the proposed bounds. The corresponding maimum relative errors of the bounds are approimately smaller than 0.7% 0.3% for the lower bound upper bound, respectively. These values show that the obtained bounds are very accurate thus are suitably applicable in the most engineering problems. Finally, we have illustrated some results in order to visualize the achieved gains. APPENDIX A PROOF OF THE BOUNDS Lemma A. The transcendental number can be bounded by the double inequality 9 3 < < 0. () Proof: Both bounds are well known for long, see for eample [0]. A new proof of the upper bound can be found in []. We here give an elementary proof of the lower bound. The identities = k= k(k) ζ() = k= k = 6, see for eample [, p. 8, eq p., eq. 0..3], are used to deduce 6 = k= k k= k(k ) = k= k (k ) = }{{ 36 } k (k ) > 9 8. (3) k= = 9 8 Hence 9 3 < follows. In the following, we denote the differences h() by f () := () h () := h(), (5) respectively. From Corollary II.6 it is immediately deduced that lim f () = lim h() ±0 ±0 = lim f () = lim h() = 0. (6) ± ± By direct algebra the first derivatives of f () h () are given as d f () d d h () d = ( ) ( ) ( ( ) ) (7) = ( 6 6 ) ( 6 ) ( 6 respectively. ( 6 ) ), (8) Corollary A. The first derivatives of f () h () vanish only at three real points, namely { f 0, ± ( ) } (9) 6 respectively. h { } , ± 576 (0, (30) ) Proof: We set (7) (8) equal to zero obtain the points in (9) (30) by direct calculations. It remains to prove that all points are real. This is done by showing that the discriminant functions y f (ν) := ν 36ν 60 = (ν 8)(0 ν ) (3) y h (ν) := 5ν 08ν 576 = (5ν 8)( ν ) (3) are non-negative for ν =. A curve tracing of y f (ν) y h (ν) leads to the relationships y f (ν) 0 8 ν 0 (33) y h (ν) ν, (3)

5 5 respectively. Hence, both y f (ν) y h (ν) are non-negative for all 8 5 ν 0. By comparing the latter double inequality with the double inequality in Lemma A. we deduce that y f () y h () are non-negative, hence, all roots in (9) (30) are real. Corollary A.3 The difference f () is positive for all sufficiently small positive real numbers. For all negative real numbers with sufficiently small absolute value, the difference f () is negative. Proof: We incorporate the equations (8) (9) into () to derive the first-order approimation of f () as f () 0 3( ) 3 O ( 5) (35) for all sufficiently small values of. From the double inequality in Lemma A., we deduce that the last ratio is always positive which completes the proof. Corollary A. The difference h () is positive for all sufficiently large positive real numbers. For all negative real numbers with sufficiently large absolute value, the difference h () is negative. Proof: We incorporate the equations () (3) into (5) to derive the first-order asymptotic approimation of h () as h () 0 O ( ) (36) for all sufficiently large values of. From the double inequality in Lemma A., we deduce that the last ratio is always positive which completes the proof. Proof of THEOREM II.: We only consider the case of 0. The case of 0 can be proved analogously, due to the point symmetric property of all functions in Theorem II.. On the one h, we know from Corollary A. that each of differences f () h () has only one stationary point for all > 0. On the other h, each of them attains equal values at = 0 as, i.e., f (0) = f ( ) = 0 h (0) = h ( ) = 0, according to the equation (6). Hence, because of Corollary A.3 A., as increases from zero to infinity each of the differences f () h () increases monotonically from zero to a maimum value from there on decreases monotonically toward zero. Thus, both differences f () h () are non-negative for all 0. In other words, if one of the differences had at least one sign change for some value of > 0, then it would have at least two stationary points for > 0, but this contradicts the curve tracing in Corollary A.. APPENDIX B PROOF OF THE RELATIVE ERRORS Definition B. Let r f () r h () be defined as in Definition II.. Then, we define two auiliary sets by I f := { R r f () r h () } (37) I h := { R r f () < r h () }. (38) Note that both sets I f I h are disjoint their union is the whole real domain. Corollary B. For all I f, 0, the inequality holds. If I h, 0, then the inequality (39) < (0) holds. In the case of I f with < 0 I h with < 0 the above inequalities are reversed. Proof: For all I f with 0, from Definition II. B. it follows that r f () r h () r f () r h () h(). () Similarly, for all I h with 0 it follows that r f () < r h () r f () < r h () < h() <. () In the case of < 0, the functions, h() are negative, hence, the inequalities in (39) (0) are reversed. Proof of THEOREM II.5: The proof of inequality (0) follows from inequality (7) Definition II.. It gives r f () = r h () = h() which in turn result in ma { r f (), r h () } (3) (). (5) The proof of inequality () follows from Definition II. Corollary B.. For all I f with 0, it gives r f () = r h () = h() which in turn result in r h () h() h() h() = = (6) (7) r f (). (8)

6 6 If I h with 0, then it gives r f () = < r h () = h() > which in turn result in r f () < h() h() h() = = (9) (50) < r h(). (5) From the double inequalities (8) (5), we deduce that min { r f (), r h () } ma{ r f (), r h () } (5) for all 0. For the case of < 0, the proof can be obtained analogously. The identities in (0) () arise from straightforward calculations. ACKNOWLEDGMENT Research described in the present work was supervised by Univ.-Prof. Dr. rer. nat. R. Mathar, Institute for Theoretical Information Technology, RWTH Aachen University. The author would like to thank him for his professional advice patience. REFERENCES [] J. E. Volder, The cordic trigonometric computing technique, Electronic Computers, IRE Transactions on, vol. EC-8, no. 3, pp , 959. [] R. Schmidt, Multiple emitter location signal parameter estimation, Antennas Propagation, IEEE Transactions on, vol. 3, no. 3, pp , 986. [3] H. Fu P.-Y. Kam, MAP/ML estimation of the frequency phase of a single sinusoid in noise, Signal Processing, IEEE Transactions on, vol. 55, no. 3, pp , 007. [] X.-B. Zeng, Q.-M. Hu, J.-M. He, Q.-P. Tu, X.-J. Yu, High power RF amplifier s new nonlinear models, in Microwave Conference Proceedings, 005. APMC 005. Asia-Pacific Conference Proceedings, vol., 005. [5] B. Widrow M. Lehr, 30 years of adaptive neural networks: perceptron, madaline, backpropagation, Proceedings of the IEEE, vol. 78, no. 9, pp. 5, 990. [6] C. Hwang H.-C. Chow, Simplification of z-transfer function via Padé approimation of tangent phase function, Automatic Control, IEEE Transactions on, vol. 30, no., pp. 0 0, 985. [7] R. E. Shafer, Elementary problems: E 867, The American Mathematical Monthly, vol. 73, no. 3, p. 309, 966. [8] F. Qi, S.-Q. Zhang, B.-N. Guo, Sharpening generalizations of Shafer s inequality for the arc tangent function, Journal of Inequalities Applications, vol. 009, 009. [9] N. Bleistein R. A. Helsman, Asymptotic Epansions of Integrals. New York: Dover Publications, 986. [0] O. Neugebauer, The Eact Sciences in Antiquity. Dover Publications Incorporated, 969. [] N. D. Elkies, Why is so close to 0? The American Mathematical Monthly, vol. 0, no. 7, p. 59, 003. [] I. S. Gradshteyn I. M. Ryzhik, Table of Integrals, Series, Products, 7th ed. London: Academic Press, 007.

Approximations to inverse tangent function

Approximations to inverse tangent function Qiao Chen Journal of Inequalities Applications (2018 2018:141 https://doiorg/101186/s13660-018-1734-7 R E S E A R C H Open Access Approimations to inverse tangent function Quan-Xi Qiao 1 Chao-Ping Chen

More information

Piecewise Smooth Solutions to the Burgers-Hilbert Equation

Piecewise Smooth Solutions to the Burgers-Hilbert Equation Piecewise Smooth Solutions to the Burgers-Hilbert Equation Alberto Bressan and Tianyou Zhang Department of Mathematics, Penn State University, University Park, Pa 68, USA e-mails: bressan@mathpsuedu, zhang

More information

Part I Analysis in Economics

Part I Analysis in Economics Part I Analysis in Economics D 1 1 (Function) A function f from a set A into a set B, denoted by f : A B, is a correspondence that assigns to each element A eactly one element y B We call y the image of

More information

November 13, 2018 MAT186 Week 8 Justin Ko

November 13, 2018 MAT186 Week 8 Justin Ko 1 Mean Value Theorem Theorem 1 (Mean Value Theorem). Let f be a continuous on [a, b] and differentiable on (a, b). There eists a c (a, b) such that f f(b) f(a) (c) =. b a Eample 1: The Mean Value Theorem

More information

Some Expectations of a Non-Central Chi-Square Distribution With an Even Number of Degrees of Freedom

Some Expectations of a Non-Central Chi-Square Distribution With an Even Number of Degrees of Freedom Some Expectations of a Non-Central Chi-Square Distribution With an Even Number of Degrees of Freedom Stefan M. Moser April 7, 007 Abstract The non-central chi-square distribution plays an important role

More information

A Basic Course in Real Analysis Prof. P. D. Srivastava Department of Mathematics Indian Institute of Technology, Kharagpur

A Basic Course in Real Analysis Prof. P. D. Srivastava Department of Mathematics Indian Institute of Technology, Kharagpur A Basic Course in Real Analysis Prof. P. D. Srivastava Department of Mathematics Indian Institute of Technology, Kharagpur Lecture - 36 Application of MVT, Darbou Theorem, L Hospital Rule (Refer Slide

More information

The incomplete gamma functions. Notes by G.J.O. Jameson. These notes incorporate the Math. Gazette article [Jam1], with some extra material.

The incomplete gamma functions. Notes by G.J.O. Jameson. These notes incorporate the Math. Gazette article [Jam1], with some extra material. The incomplete gamma functions Notes by G.J.O. Jameson These notes incorporate the Math. Gazette article [Jam], with some etra material. Definitions and elementary properties functions: Recall the integral

More information

+ 2 on the interval [-1,3]

+ 2 on the interval [-1,3] Section.1 Etrema on an Interval 1. Understand the definition of etrema of a function on an interval.. Understand the definition of relative etrema of a function on an open interval.. Find etrema on a closed

More information

4.3 - How Derivatives Affect the Shape of a Graph

4.3 - How Derivatives Affect the Shape of a Graph 4.3 - How Derivatives Affect the Shape of a Graph 1. Increasing and Decreasing Functions Definition: A function f is (strictly) increasing on an interval I if for every 1, in I with 1, f 1 f. A function

More information

1.4 FOUNDATIONS OF CONSTRAINED OPTIMIZATION

1.4 FOUNDATIONS OF CONSTRAINED OPTIMIZATION Essential Microeconomics -- 4 FOUNDATIONS OF CONSTRAINED OPTIMIZATION Fundamental Theorem of linear Programming 3 Non-linear optimization problems 6 Kuhn-Tucker necessary conditions Sufficient conditions

More information

Elementary properties of the gamma function

Elementary properties of the gamma function Appendi G Elementary properties of the gamma function G.1 Introduction The elementary definition of the gamma function is Euler s integral: 1 Γ(z) = 0 t z 1 e t. (G.1) For the sake of convergence of the

More information

Sigmoid functions for the smooth approximation to x

Sigmoid functions for the smooth approximation to x Sigmoid functions for the smooth approimation to Yogesh J. Bagul,, Christophe Chesneau,, Department of Mathematics, K. K. M. College Manwath, Dist : Parbhani(M.S.) - 43505, India. Email : yjbagul@gmail.com

More information

Harmonic Analysis Homework 5

Harmonic Analysis Homework 5 Harmonic Analysis Homework 5 Bruno Poggi Department of Mathematics, University of Minnesota November 4, 6 Notation Throughout, B, r is the ball of radius r with center in the understood metric space usually

More information

Chiang/Wainwright: Fundamental Methods of Mathematical Economics

Chiang/Wainwright: Fundamental Methods of Mathematical Economics Chiang/Wainwright: Fundamental Methods of Mathematical Economics CHAPTER 9 EXERCISE 9.. Find the stationary values of the following (check whether they are relative maima or minima or inflection points),

More information

Section 3.3 Limits Involving Infinity - Asymptotes

Section 3.3 Limits Involving Infinity - Asymptotes 76 Section. Limits Involving Infinity - Asymptotes We begin our discussion with analyzing its as increases or decreases without bound. We will then eplore functions that have its at infinity. Let s consider

More information

ON THE HILBERT INEQUALITY. 1. Introduction. π 2 a m + n. is called the Hilbert inequality for double series, where n=1.

ON THE HILBERT INEQUALITY. 1. Introduction. π 2 a m + n. is called the Hilbert inequality for double series, where n=1. Acta Math. Univ. Comenianae Vol. LXXVII, 8, pp. 35 3 35 ON THE HILBERT INEQUALITY ZHOU YU and GAO MINGZHE Abstract. In this paper it is shown that the Hilbert inequality for double series can be improved

More information

Yunhi Cho. 3 y = y yy,.

Yunhi Cho. 3 y = y yy,. Kangweon-Kyungki Math. Jour. 12 (2004), No. 1, pp. 55 65 THE DIFFERENTIAL PROPERTY OF ODD AND EVEN HYPERPOWER FUNCTIONS Yunhi Cho Abstract. Let h e (y), h o (y) denote the limits of the sequences { 2n

More information

Integrals of the form. Notes by G.J.O. Jameson. I f (x) = x. f(t) cos t dt, S f (x) = x

Integrals of the form. Notes by G.J.O. Jameson. I f (x) = x. f(t) cos t dt, S f (x) = x Integrals of the form f(t)eit dt Notes by G.J.O. Jameson Basic results We consider integrals of the form with real and imaginary parts I f () = f(t)e it dt, C f () = f(t) cos t dt, S f () = f(t) sin t

More information

Finite difference modelling, Fourier analysis, and stability

Finite difference modelling, Fourier analysis, and stability Finite difference modelling, Fourier analysis, and stability Peter M. Manning and Gary F. Margrave ABSTRACT This paper uses Fourier analysis to present conclusions about stability and dispersion in finite

More information

Estimates for probabilities of independent events and infinite series

Estimates for probabilities of independent events and infinite series Estimates for probabilities of independent events and infinite series Jürgen Grahl and Shahar evo September 9, 06 arxiv:609.0894v [math.pr] 8 Sep 06 Abstract This paper deals with finite or infinite sequences

More information

CALCULUS APPLICATIONS OF DIFFERENTIATION LESSON PLAN. C3 Topic Overview

CALCULUS APPLICATIONS OF DIFFERENTIATION LESSON PLAN. C3 Topic Overview CALCULUS C3 Topic Overview C3 APPLICATIONS OF DIFFERENTIATION Differentiation can be used to investigate the behaviour of a function, to find regions where the value of a function is increasing or decreasing

More information

NEW ASYMPTOTIC EXPANSION AND ERROR BOUND FOR STIRLING FORMULA OF RECIPROCAL GAMMA FUNCTION

NEW ASYMPTOTIC EXPANSION AND ERROR BOUND FOR STIRLING FORMULA OF RECIPROCAL GAMMA FUNCTION M athematical Inequalities & Applications Volume, Number 4 8), 957 965 doi:.753/mia-8--65 NEW ASYMPTOTIC EXPANSION AND ERROR BOUND FOR STIRLING FORMULA OF RECIPROCAL GAMMA FUNCTION PEDRO J. PAGOLA Communicated

More information

6.034f Neural Net Notes October 28, 2010

6.034f Neural Net Notes October 28, 2010 6.034f Neural Net Notes October 28, 2010 These notes are a supplement to material presented in lecture. I lay out the mathematics more prettily and etend the analysis to handle multiple-neurons per layer.

More information

1 Exponential Functions Limit Derivative Integral... 5

1 Exponential Functions Limit Derivative Integral... 5 Contents Eponential Functions 3. Limit................................................. 3. Derivative.............................................. 4.3 Integral................................................

More information

Some Properties of Convex Functions

Some Properties of Convex Functions Filomat 31:10 2017), 3015 3021 https://doi.org/10.2298/fil1710015a Published by Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.rs/filomat Some Properties

More information

Lecture 4b. Bessel functions. Introduction. Generalized factorial function. 4b.1. Using integration by parts it is easy to show that

Lecture 4b. Bessel functions. Introduction. Generalized factorial function. 4b.1. Using integration by parts it is easy to show that 4b. Lecture 4b Using integration by parts it is easy to show that Bessel functions Introduction In the previous lecture the separation of variables method led to Bessel's equation y' ' y ' 2 y= () 2 Here

More information

x y x 2 2 x y x x y x U x y x y

x y x 2 2 x y x x y x U x y x y Lecture 7 Appendi B: Some sample problems from Boas Here are some solutions to the sample problems assigned for hapter 4 4: 8 Solution: We want to learn about the analyticity properties of the function

More information

A rational approximation of the arctangent function and a new approach in computing pi

A rational approximation of the arctangent function and a new approach in computing pi arxiv:63.33v math.gm] 4 Mar 6 A rational approimation of the arctangent function and a new approach in computing pi S. M. Abrarov and B. M. Quine March 4, 6 Abstract We have shown recently that integration

More information

Universidad Carlos III de Madrid

Universidad Carlos III de Madrid Universidad Carlos III de Madrid Eercise 1 2 3 4 5 6 Total Points Department of Economics Mathematics I Final Eam January 22nd 2018 LAST NAME: Eam time: 2 hours. FIRST NAME: ID: DEGREE: GROUP: 1 (1) Consider

More information

5.6 Asymptotes; Checking Behavior at Infinity

5.6 Asymptotes; Checking Behavior at Infinity 5.6 Asymptotes; Checking Behavior at Infinity checking behavior at infinity DEFINITION asymptote In this section, the notion of checking behavior at infinity is made precise, by discussing both asymptotes

More information

1 Question related to polynomials

1 Question related to polynomials 07-08 MATH00J Lecture 6: Taylor Series Charles Li Warning: Skip the material involving the estimation of error term Reference: APEX Calculus This lecture introduced Taylor Polynomial and Taylor Series

More information

HW #1 SOLUTIONS. g(x) sin 1 x

HW #1 SOLUTIONS. g(x) sin 1 x HW #1 SOLUTIONS 1. Let f : [a, b] R and let v : I R, where I is an interval containing f([a, b]). Suppose that f is continuous at [a, b]. Suppose that lim s f() v(s) = v(f()). Using the ɛ δ definition

More information

Tail Approximation of the Skew-Normal by the Skew-Normal Laplace: Application to Owen s T Function and the Bivariate Normal Distribution

Tail Approximation of the Skew-Normal by the Skew-Normal Laplace: Application to Owen s T Function and the Bivariate Normal Distribution Journal of Statistical and Econometric ethods vol. no. 3 - ISS: 5-557 print version 5-565online Scienpress Ltd 3 Tail Approimation of the Skew-ormal by the Skew-ormal Laplace: Application to Owen s T Function

More information

9.8 APPLICATIONS OF TAYLOR SERIES EXPLORATORY EXERCISES. Using Taylor Polynomials to Approximate a Sine Value EXAMPLE 8.1

9.8 APPLICATIONS OF TAYLOR SERIES EXPLORATORY EXERCISES. Using Taylor Polynomials to Approximate a Sine Value EXAMPLE 8.1 9-75 SECTION 9.8.. Applications of Taylor Series 677 and f 0) miles/min 3. Predict the location of the plane at time t min. 5. Suppose that an astronaut is at 0, 0) and the moon is represented by a circle

More information

THS Step By Step Calculus Chapter 1

THS Step By Step Calculus Chapter 1 Name: Class Period: Throughout this packet there will be blanks you are epected to fill in prior to coming to class. This packet follows your Larson Tetbook. Do NOT throw away! Keep in 3 ring binder until

More information

2008 CALCULUS AB SECTION I, Part A Time 55 minutes Number of Questions 28 A CALCULATOR MAY NOT BE USED ON THIS PART OF THE EXAMINATION

2008 CALCULUS AB SECTION I, Part A Time 55 minutes Number of Questions 28 A CALCULATOR MAY NOT BE USED ON THIS PART OF THE EXAMINATION 8 CALCULUS AB SECTION I, Part A Time 55 minutes Number of Questions 8 A CALCULATOR MAY NOT BE USED ON THIS PART OF THE EXAMINATION Directions: Solve each of the following problems. After eamining the form

More information

arxiv: v1 [math.ca] 2 Nov 2018

arxiv: v1 [math.ca] 2 Nov 2018 arxiv.org arxiv:1811.00748v1 [math.ca] 2 Nov 2018 One method for proving some classes of eponential analytical inequalities Branko Malešević a, Tatjana Lutovac a, Bojan Banjac b a School of Electrical

More information

APPROXIMATIONS FOR SOME FUNCTIONS OF PRIMES

APPROXIMATIONS FOR SOME FUNCTIONS OF PRIMES APPROXIMATIONS FOR SOME FUNCTIONS OF PRIMES Fataneh Esteki B. Sc., University of Isfahan, 2002 A Thesis Submitted to the School of Graduate Studies of the University of Lethbridge in Partial Fulfillment

More information

COMPLETE MONOTONICITIES OF FUNCTIONS INVOLVING THE GAMMA AND DIGAMMA FUNCTIONS. 1. Introduction

COMPLETE MONOTONICITIES OF FUNCTIONS INVOLVING THE GAMMA AND DIGAMMA FUNCTIONS. 1. Introduction COMPLETE MONOTONICITIES OF FUNCTIONS INVOLVING THE GAMMA AND DIGAMMA FUNCTIONS FENG QI AND BAI-NI GUO Abstract. In the article, the completely monotonic results of the functions [Γ( + 1)] 1/, [Γ(+α+1)]1/(+α),

More information

Calculation of Cylindrical Functions using Correction of Asymptotic Expansions

Calculation of Cylindrical Functions using Correction of Asymptotic Expansions Universal Journal of Applied Mathematics & Computation (4), 5-46 www.papersciences.com Calculation of Cylindrical Functions using Correction of Asymptotic Epansions G.B. Muravskii Faculty of Civil and

More information

IN THIS PAPER, we consider a class of continuous-time recurrent

IN THIS PAPER, we consider a class of continuous-time recurrent IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 51, NO. 4, APRIL 2004 161 Global Output Convergence of a Class of Continuous-Time Recurrent Neural Networks With Time-Varying Thresholds

More information

Review of Optimization Basics

Review of Optimization Basics Review of Optimization Basics. Introduction Electricity markets throughout the US are said to have a two-settlement structure. The reason for this is that the structure includes two different markets:

More information

Review: Limits of Functions - 10/7/16

Review: Limits of Functions - 10/7/16 Review: Limits of Functions - 10/7/16 1 Right and Left Hand Limits Definition 1.0.1 We write lim a f() = L to mean that the function f() approaches L as approaches a from the left. We call this the left

More information

Hyperbolic Systems of Conservation Laws. in One Space Dimension. II - Solutions to the Cauchy problem. Alberto Bressan

Hyperbolic Systems of Conservation Laws. in One Space Dimension. II - Solutions to the Cauchy problem. Alberto Bressan Hyperbolic Systems of Conservation Laws in One Space Dimension II - Solutions to the Cauchy problem Alberto Bressan Department of Mathematics, Penn State University http://www.math.psu.edu/bressan/ 1 Global

More information

Taylor Series and Asymptotic Expansions

Taylor Series and Asymptotic Expansions Taylor Series and Asymptotic Epansions The importance of power series as a convenient representation, as an approimation tool, as a tool for solving differential equations and so on, is pretty obvious.

More information

Absolute and Local Extrema. Critical Points In the proof of Rolle s Theorem, we actually demonstrated the following

Absolute and Local Extrema. Critical Points In the proof of Rolle s Theorem, we actually demonstrated the following Absolute and Local Extrema Definition 1 (Absolute Maximum). A function f has an absolute maximum at c S if f(x) f(c) x S. We call f(c) the absolute maximum of f on S. Definition 2 (Local Maximum). A function

More information

Integral Theory. Prof. Dr. W. P. Kowalk Universität Oldenburg Version 2 from 29/04/08

Integral Theory. Prof. Dr. W. P. Kowalk Universität Oldenburg Version 2 from 29/04/08 Integral Theory Prof. Dr. W. P. Kowalk Universität Oldenburg Version 2 from 29/4/8 All rights reserved. This manuscript may be used as presented, copied in all or in part, and distributed everywhere, as

More information

MATH 1A, Complete Lecture Notes. Fedor Duzhin

MATH 1A, Complete Lecture Notes. Fedor Duzhin MATH 1A, Complete Lecture Notes Fedor Duzhin 2007 Contents I Limit 6 1 Sets and Functions 7 1.1 Sets................................. 7 1.2 Functions.............................. 8 1.3 How to define a

More information

LECTURE # - NEURAL COMPUTATION, Feb 04, Linear Regression. x 1 θ 1 output... θ M x M. Assumes a functional form

LECTURE # - NEURAL COMPUTATION, Feb 04, Linear Regression. x 1 θ 1 output... θ M x M. Assumes a functional form LECTURE # - EURAL COPUTATIO, Feb 4, 4 Linear Regression Assumes a functional form f (, θ) = θ θ θ K θ (Eq) where = (,, ) are the attributes and θ = (θ, θ, θ ) are the function parameters Eample: f (, θ)

More information

Stability and bifurcation of a simple neural network with multiple time delays.

Stability and bifurcation of a simple neural network with multiple time delays. Fields Institute Communications Volume, 999 Stability and bifurcation of a simple neural network with multiple time delays. Sue Ann Campbell Department of Applied Mathematics University of Waterloo Waterloo

More information

C) 2 D) 4 E) 6. ? A) 0 B) 1 C) 1 D) The limit does not exist.

C) 2 D) 4 E) 6. ? A) 0 B) 1 C) 1 D) The limit does not exist. . The asymptotes of the graph of the parametric equations = t, y = t t + are A) =, y = B) = only C) =, y = D) = only E) =, y =. What are the coordinates of the inflection point on the graph of y = ( +

More information

MATH39001 Generating functions. 1 Ordinary power series generating functions

MATH39001 Generating functions. 1 Ordinary power series generating functions MATH3900 Generating functions The reference for this part of the course is generatingfunctionology by Herbert Wilf. The 2nd edition is downloadable free from http://www.math.upenn. edu/~wilf/downldgf.html,

More information

Numerical Methods. Root Finding

Numerical Methods. Root Finding Numerical Methods Solving Non Linear 1-Dimensional Equations Root Finding Given a real valued function f of one variable (say ), the idea is to find an such that: f() 0 1 Root Finding Eamples Find real

More information

Brief Revision Notes and Strategies

Brief Revision Notes and Strategies Brief Revision Notes and Strategies Straight Line Distance Formula d = ( ) + ( y y ) d is distance between A(, y ) and B(, y ) Mid-point formula +, y + M y M is midpoint of A(, y ) and B(, y ) y y Equation

More information

arxiv: v1 [math.st] 26 Jun 2011

arxiv: v1 [math.st] 26 Jun 2011 The Shape of the Noncentral χ 2 Density arxiv:1106.5241v1 [math.st] 26 Jun 2011 Yaming Yu Department of Statistics University of California Irvine, CA 92697, USA yamingy@uci.edu Abstract A noncentral χ

More information

Given the vectors u, v, w and real numbers α, β, γ. Calculate vector a, which is equal to the linear combination α u + β v + γ w.

Given the vectors u, v, w and real numbers α, β, γ. Calculate vector a, which is equal to the linear combination α u + β v + γ w. Selected problems from the tetbook J. Neustupa, S. Kračmar: Sbírka příkladů z Matematiky I Problems in Mathematics I I. LINEAR ALGEBRA I.. Vectors, vector spaces Given the vectors u, v, w and real numbers

More information

Nonlinear Equations. Chapter The Bisection Method

Nonlinear Equations. Chapter The Bisection Method Chapter 6 Nonlinear Equations Given a nonlinear function f(), a value r such that f(r) = 0, is called a root or a zero of f() For eample, for f() = e 016064, Fig?? gives the set of points satisfying y

More information

Tight Bounds for Symmetric Divergence Measures and a Refined Bound for Lossless Source Coding

Tight Bounds for Symmetric Divergence Measures and a Refined Bound for Lossless Source Coding APPEARS IN THE IEEE TRANSACTIONS ON INFORMATION THEORY, FEBRUARY 015 1 Tight Bounds for Symmetric Divergence Measures and a Refined Bound for Lossless Source Coding Igal Sason Abstract Tight bounds for

More information

Research Article On Jordan Type Inequalities for Hyperbolic Functions

Research Article On Jordan Type Inequalities for Hyperbolic Functions Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 010, Article ID 6548, 14 pages doi:10.1155/010/6548 Research Article On Jordan Type Inequalities for Hyperbolic Functions

More information

UNCONSTRAINED OPTIMIZATION PAUL SCHRIMPF OCTOBER 24, 2013

UNCONSTRAINED OPTIMIZATION PAUL SCHRIMPF OCTOBER 24, 2013 PAUL SCHRIMPF OCTOBER 24, 213 UNIVERSITY OF BRITISH COLUMBIA ECONOMICS 26 Today s lecture is about unconstrained optimization. If you re following along in the syllabus, you ll notice that we ve skipped

More information

MATH section 3.4 Curve Sketching Page 1 of 29

MATH section 3.4 Curve Sketching Page 1 of 29 MATH section. Curve Sketching Page of 9 The step by step procedure below is for regular rational and polynomial functions. If a function contains radical or trigonometric term, then proceed carefully because

More information

In Praise of y = x α sin 1x

In Praise of y = x α sin 1x In Praise of y = α sin H. Turgay Kaptanoğlu Countereamples have great educational value, because they serve to illustrate the limits of mathematical facts. Every mathematics course should include countereamples

More information

Semi-Discrete Central-Upwind Schemes with Reduced Dissipation for Hamilton-Jacobi Equations

Semi-Discrete Central-Upwind Schemes with Reduced Dissipation for Hamilton-Jacobi Equations Semi-Discrete Central-Upwind Schemes with Reduced Dissipation for Hamilton-Jacobi Equations Steve Bryson, Aleander Kurganov, Doron Levy and Guergana Petrova Abstract We introduce a new family of Godunov-type

More information

The Kuhn-Tucker and Envelope Theorems

The Kuhn-Tucker and Envelope Theorems The Kuhn-Tucker and Envelope Theorems Peter Ireland ECON 77200 - Math for Economists Boston College, Department of Economics Fall 207 The Kuhn-Tucker and envelope theorems can be used to characterize the

More information

Lagrangian Duality for Dummies

Lagrangian Duality for Dummies Lagrangian Duality for Dummies David Knowles November 13, 2010 We want to solve the following optimisation problem: f 0 () (1) such that f i () 0 i 1,..., m (2) For now we do not need to assume conveity.

More information

Research Article Inequalities for Hyperbolic Functions and Their Applications

Research Article Inequalities for Hyperbolic Functions and Their Applications Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2010, Article ID 130821, 10 pages doi:10.1155/2010/130821 Research Article Inequalities for Hyperbolic Functions and Their

More information

1 Kernel methods & optimization

1 Kernel methods & optimization Machine Learning Class Notes 9-26-13 Prof. David Sontag 1 Kernel methods & optimization One eample of a kernel that is frequently used in practice and which allows for highly non-linear discriminant functions

More information

Approximate Analytical Calculation of the Skin Effect in Rectangular Wires

Approximate Analytical Calculation of the Skin Effect in Rectangular Wires Approimate Analytical Calculation of the Skin Effect in Rectangular Wires Dieter Gerling University of Federal Defense Munich, 85579 Neubiberg, Germany Dieter.Gerling@unibw.de Abstract- It is well-known

More information

Chapter (AB/BC, non-calculator) (a) Find the critical numbers of g. (b) For what values of x is g increasing? Justify your answer.

Chapter (AB/BC, non-calculator) (a) Find the critical numbers of g. (b) For what values of x is g increasing? Justify your answer. Chapter 3 1. (AB/BC, non-calculator) Given g ( ) 2 4 3 6 : (a) Find the critical numbers of g. (b) For what values of is g increasing? Justify your answer. (c) Identify the -coordinate of the critical

More information

NOTES 5: APPLICATIONS OF DIFFERENTIATION

NOTES 5: APPLICATIONS OF DIFFERENTIATION NOTES 5: APPLICATIONS OF DIFFERENTIATION Name: Date: Period: Mrs. Nguyen s Initial: LESSON 5.1 EXTREMA ON AN INTERVAL Definition of Etrema Let f be defined on an interval I containing c. 1. f () c is the

More information

Nonlinear eigenvalue problems for higher order model equations

Nonlinear eigenvalue problems for higher order model equations Nonlinear eigenvalue problems for higher order model equations L.A. Peletier Mathematical Institute, Leiden University PB 951, 3 RA Leiden, The Netherlands e-mail: peletier@math.leidenuniv.nl 1 Introduction

More information

Functions of Several Variables

Functions of Several Variables Chapter 1 Functions of Several Variables 1.1 Introduction A real valued function of n variables is a function f : R, where the domain is a subset of R n. So: for each ( 1,,..., n ) in, the value of f is

More information

Solutions to Problem Sheet for Week 6

Solutions to Problem Sheet for Week 6 THE UNIVERSITY OF SYDNEY SCHOOL OF MATHEMATICS AND STATISTICS Solutions to Problem Sheet for Week 6 MATH90: Differential Calculus (Advanced) Semester, 07 Web Page: sydney.edu.au/science/maths/u/ug/jm/math90/

More information

On the least values of L p -norms for the Kontorovich Lebedev transform and its convolution

On the least values of L p -norms for the Kontorovich Lebedev transform and its convolution Journal of Approimation Theory 131 4 31 4 www.elsevier.com/locate/jat On the least values of L p -norms for the Kontorovich Lebedev transform and its convolution Semyon B. Yakubovich Department of Pure

More information

Limits: How to approach them?

Limits: How to approach them? Limits: How to approach them? The purpose of this guide is to show you the many ways to solve it problems. These depend on many factors. The best way to do this is by working out a few eamples. In particular,

More information

Algebra y funciones [219 marks]

Algebra y funciones [219 marks] Algebra y funciones [9 marks] Let f() = 3 ln and g() = ln5 3. a. Epress g() in the form f() + lna, where a Z +. attempt to apply rules of logarithms e.g. ln a b = b lna, lnab = lna + lnb correct application

More information

The binary entropy function

The binary entropy function ECE 7680 Lecture 2 Definitions and Basic Facts Objective: To learn a bunch of definitions about entropy and information measures that will be useful through the quarter, and to present some simple but

More information

Multidimensional partitions of unity and Gaussian terrains

Multidimensional partitions of unity and Gaussian terrains and Gaussian terrains Richard A. Bale, Jeff P. Grossman, Gary F. Margrave, and Michael P. Lamoureu ABSTRACT Partitions of unity play an important rôle as amplitude-preserving windows for nonstationary

More information

Mills ratio: Monotonicity patterns and functional inequalities

Mills ratio: Monotonicity patterns and functional inequalities J. Math. Anal. Appl. 340 (008) 136 1370 www.elsevier.com/locate/jmaa Mills ratio: Monotonicity patterns and functional inequalities Árpád Baricz Babeş-Bolyai University, Faculty of Economics, RO-400591

More information

Calculus 1 - Lab ) f(x) = 1 x. 3.8) f(x) = arcsin( x+1., prove the equality cosh 2 x sinh 2 x = 1. Calculus 1 - Lab ) lim. 2.

Calculus 1 - Lab ) f(x) = 1 x. 3.8) f(x) = arcsin( x+1., prove the equality cosh 2 x sinh 2 x = 1. Calculus 1 - Lab ) lim. 2. ) Solve the following inequalities.) ++.) 4 >.) Calculus - Lab { + > + 5 + < +. ) Graph the functions f() =, g() = + +, h() = cos( ), r() = +. ) Find the domain of the following functions.) f() = +.) f()

More information

Evaluation of integrals by differentiation with respect to a parameter

Evaluation of integrals by differentiation with respect to a parameter December 8 Evaluation of integrals by differentiation with respect to a parameter Khristo N Boyadzhiev Department of Mathematics and Statistics, Ohio Northern University, Ada, OH 458, USA k-boyadzhiev@onuedu

More information

So, t = 1 is a point of inflection of s(). Use s () t to find the velocity at t = Because 0, use 144.

So, t = 1 is a point of inflection of s(). Use s () t to find the velocity at t = Because 0, use 144. AP Eam Practice Questions for Chapter AP Eam Practice Questions for Chapter f 4 + 6 7 9 f + 7 0 + 6 0 ( + )( ) 0,. The critical numbers of f( ) are and.. Evaluate each point. A: d d C: d d B: D: d d d

More information

Collatz cycles with few descents

Collatz cycles with few descents ACTA ARITHMETICA XCII.2 (2000) Collatz cycles with few descents by T. Brox (Stuttgart) 1. Introduction. Let T : Z Z be the function defined by T (x) = x/2 if x is even, T (x) = (3x + 1)/2 if x is odd.

More information

ON SOME GENERALIZED NORM TRIANGLE INEQUALITIES. 1. Introduction In [3] Dragomir gave the following bounds for the norm of n. x j.

ON SOME GENERALIZED NORM TRIANGLE INEQUALITIES. 1. Introduction In [3] Dragomir gave the following bounds for the norm of n. x j. Rad HAZU Volume 515 (2013), 43-52 ON SOME GENERALIZED NORM TRIANGLE INEQUALITIES JOSIP PEČARIĆ AND RAJNA RAJIĆ Abstract. In this paper we characterize equality attainedness in some recently obtained generalized

More information

Received 24 November 2015; accepted 17 January 2016; published 20 January 2016

Received 24 November 2015; accepted 17 January 2016; published 20 January 2016 Applied Mathematics, 06, 7, 40-60 Published Online January 06 in SciRes. http://www.scirp.org/journal/am http://d.doi.org/0.436/am.06.7004 The Formulas to Compare the Convergences of Newton s Method and

More information

THE information capacity is one of the most important

THE information capacity is one of the most important 256 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 44, NO. 1, JANUARY 1998 Capacity of Two-Layer Feedforward Neural Networks with Binary Weights Chuanyi Ji, Member, IEEE, Demetri Psaltis, Senior Member,

More information

Harmonic sets and the harmonic prime number theorem

Harmonic sets and the harmonic prime number theorem Harmonic sets and the harmonic prime number theorem Version: 9th September 2004 Kevin A. Broughan and Rory J. Casey University of Waikato, Hamilton, New Zealand E-mail: kab@waikato.ac.nz We restrict primes

More information

Rates of Convergence to Self-Similar Solutions of Burgers Equation

Rates of Convergence to Self-Similar Solutions of Burgers Equation Rates of Convergence to Self-Similar Solutions of Burgers Equation by Joel Miller Andrew Bernoff, Advisor Advisor: Committee Member: May 2 Department of Mathematics Abstract Rates of Convergence to Self-Similar

More information

Artificial Neuron (Perceptron)

Artificial Neuron (Perceptron) 9/6/208 Gradient Descent (GD) Hantao Zhang Deep Learning with Python Reading: https://en.wikipedia.org/wiki/gradient_descent Artificial Neuron (Perceptron) = w T = w 0 0 + + w 2 2 + + w d d where

More information

= 1 2 x (x 1) + 1 {x} (1 {x}). [t] dt = 1 x (x 1) + O (1), [t] dt = 1 2 x2 + O (x), (where the error is not now zero when x is an integer.

= 1 2 x (x 1) + 1 {x} (1 {x}). [t] dt = 1 x (x 1) + O (1), [t] dt = 1 2 x2 + O (x), (where the error is not now zero when x is an integer. Problem Sheet,. i) Draw the graphs for [] and {}. ii) Show that for α R, α+ α [t] dt = α and α+ α {t} dt =. Hint Split these integrals at the integer which must lie in any interval of length, such as [α,

More information

DIFFERENTIATING UNDER THE INTEGRAL SIGN

DIFFERENTIATING UNDER THE INTEGRAL SIGN DIFFEENTIATING UNDE THE INTEGAL SIGN KEITH CONAD I had learned to do integrals by various methods shown in a book that my high school physics teacher Mr. Bader had given me. [It] showed how to differentiate

More information

Euler s Formula for.2n/

Euler s Formula for.2n/ Euler s Formula for.2n/ Timothy W. Jones January 28, 208 Abstract In this article we derive a formula for zeta(2) and zeta(2n). Introduction In this paper we derive from scratch k 2 D 2 6 () and k 2p D.

More information

Pre-Calculus and Trigonometry Capacity Matrix

Pre-Calculus and Trigonometry Capacity Matrix Pre-Calculus and Capacity Matri Review Polynomials A1.1.4 A1.2.5 Add, subtract, multiply and simplify polynomials and rational epressions Solve polynomial equations and equations involving rational epressions

More information

Intro to Neural Networks and Deep Learning

Intro to Neural Networks and Deep Learning Intro to Neural Networks and Deep Learning Jack Lanchantin Dr. Yanjun Qi UVA CS 6316 1 Neurons 1-Layer Neural Network Multi-layer Neural Network Loss Functions Backpropagation Nonlinearity Functions NNs

More information

FORCING WITH SEQUENCES OF MODELS OF TWO TYPES

FORCING WITH SEQUENCES OF MODELS OF TWO TYPES FORCING WITH SEQUENCES OF MODELS OF TWO TYPES ITAY NEEMAN Abstract. We present an approach to forcing with finite sequences of models that uses models of two types. This approach builds on earlier work

More information

2.29 Numerical Fluid Mechanics Spring 2015 Lecture 4

2.29 Numerical Fluid Mechanics Spring 2015 Lecture 4 2.29 Spring 2015 Lecture 4 Review Lecture 3 Truncation Errors, Taylor Series and Error Analysis Taylor series: 2 3 n n i1 i i i i i n f( ) f( ) f '( ) f ''( ) f '''( )... f ( ) R 2! 3! n! n1 ( n1) Rn f

More information

Optimization. 1 Some Concepts and Terms

Optimization. 1 Some Concepts and Terms ECO 305 FALL 2003 Optimization 1 Some Concepts and Terms The general mathematical problem studied here is how to choose some variables, collected into a vector =( 1, 2,... n ), to maimize, or in some situations

More information

Fourier Analysis Fourier Series C H A P T E R 1 1

Fourier Analysis Fourier Series C H A P T E R 1 1 C H A P T E R Fourier Analysis 474 This chapter on Fourier analysis covers three broad areas: Fourier series in Secs...4, more general orthonormal series called Sturm iouville epansions in Secs..5 and.6

More information

1 Convexity, Convex Relaxations, and Global Optimization

1 Convexity, Convex Relaxations, and Global Optimization 1 Conveity, Conve Relaations, and Global Optimization Algorithms 1 1.1 Minima Consider the nonlinear optimization problem in a Euclidean space: where the feasible region. min f(), R n Definition (global

More information

The Kuhn-Tucker and Envelope Theorems

The Kuhn-Tucker and Envelope Theorems The Kuhn-Tucker and Envelope Theorems Peter Ireland EC720.01 - Math for Economists Boston College, Department of Economics Fall 2010 The Kuhn-Tucker and envelope theorems can be used to characterize the

More information